OpenRaiser/Intern-Atlas huggingface dataset:
Non-readily identifiable data collaboration analysis for multiple datasets including personal information
Venue: arXiv cs.LG 2022
Year: 2022

Multi-source data fusion, in which multiple data sources are jointly analyzed to obtain improved information, has considerable research attention. For the datasets of multiple medical institutions, data confidentiality and cross-institutional communication are critical. In such cases, data collaboration (DC) analysis by sharing dimensionality-reduced intermediate representations without iterative cross-institutional communications may be appropriate. Identifiability of the shared data is essential when analyzing data including personal information. In this study, the identifiability of the DC analysis is investigated. The results reveals that the shared intermediate representations are readily identifiable to the original data for supervised learning. This study then proposes a non-readily identifiable DC analysis only sharing non-readily identifiable data for multiple medical datasets including personal information. The proposed method solves identifiability concerns based on a random sample permutation, the concept of interpretable DC analysis, and usage of functions that cannot be reconstructed. In numerical experiments on medical datasets, the proposed method exhibits a non-readily identifiability while maintaining a high recognition performance of the conventional DC analysis. For a hospital dataset, the proposed method exhibits a nine percentage point improvement regarding the recognition performance over the local analysis that uses only local dataset.
Accelerating differential evolution algorithm with Gaussian sampling based on estimating the convergence points
Venue: arXiv cs.NE 2022
Year: 2022

In this paper, we propose a simple strategy for estimating the convergence point approximately by averaging the elite sub-population. Based on this idea, we derive two methods, which are ordinary averaging strategy, and weighted averaging strategy. We also design a Gaussian sampling operator with the mean of the estimated convergence point with a certain standard deviation. This operator is combined with the traditional differential evolution algorithm (DE) to accelerate the convergence. Numerical experiments show that our proposal can accelerate the DE on most functions of 28 low-dimensional test functions on the CEC2013 Suite, and our proposal can easily be extended to combine with other population-based evolutionary algorithms with a simple modification.
Stochastic Resource Allocation for Semantic Communication-aided Virtual Transportation Networks in the Metaverse
Venue: arXiv cs.GT 2022
Year: 2022

The physical-virtual world synchronization to develop the Metaverse will require a massive transmission and exchange of data. In this paper, we introduce semantic communication for the development of virtual transportation networks in the Metaverse. Leveraging the perception capabilities of edge devices, virtual service providers (VSPs) can subscribe to their preferred edge devices to receive the semantic data of interest. However, the demands of the VSPs are highly dependent on the users that they are serving. To address the resource allocation problem amid stochastic user demand, we propose a stochastic semantic transmission scheme (SSTS) based on two-stage stochastic integer programming. Using real data captured by edge devices we deploy in Singapore, the simulation results show that SSTS can minimize the transmission cost of the VSPs while accounting for the users' demand uncertainties.
Pattern matching under DTW distance
Venue: arXiv cs.DS 2022
Year: 2022

In this work, we consider the problem of pattern matching under the dynamic time warping (DTW) distance motivated by potential applications in the analysis of biological data produced by the third generation sequencing. To measure the DTW distance between two strings, one must "warp" them, that is, double some letters in the strings to obtain two equal-lengths strings, and then sum the distances between the letters in the corresponding positions. When the distances between letters are integers, we show that for a pattern P with m runs and a text T with n runs: 1. There is an O(m + n)-time algorithm that computes all locations where the DTW distance from P to T is at most 1; 2. There is an O(kmn)-time algorithm that computes all locations where the DTW distance from P to T is at most k. As a corollary of the second result, we also derive an approximation algorithm for general metrics on the alphabet.
Ferroelectric FET-based strong physical unclonable function: a low-power, high-reliable and reconfigurable solution for Internet-of-Things security
Venue: arXiv cs.ET 2022
Year: 2022

Hardware security has been a key concern in modern information technologies. Especially, as the number of Internet-of-Things (IoT) devices grows rapidly, to protect the device security with low-cost security primitives becomes essential, among which Physical Unclonable Function (PUF) is a widely-used solution. In this paper, we propose the first FeFET-based strong PUF exploiting the cycle-to-cycle (C2C) variation of FeFETs as the entropy source. Based on the experimental measurements, the proposed PUF shows satisfying performance including high uniformity, uniqueness, reconfigurability and reliability. To resist machine-learning attack, XOR structure was introduced, and simulations show that our proposed PUF has similar resistance to existing attack models with traditional arbiter PUFs. Furthermore, our design is shown to be power-efficient, and highly robust to write voltage, temperature and device size, which makes it a competitive security solution for Internet-of-Things edge devices.
When Variable-Length Codes Meet the Field of Error Detection
Venue: arXiv cs.IT 2022
Year: 2022

Given a finite alphabet $A$ and a binary relation $τ\subseteq A^*\times A^*$, a set $X$ is $τ$-{\it independent} if $ τ(X)\cap X=\emptyset$. Given a quasi-metric $d$ over $A^*$ (in the meaning of \cite{W31}) and $k\ge 1$, we associate the relation $τ_{d,k}$ defined by $(x,y)\inτ_{d,k}$ if, and only if, $d(x,y)\le k$ \cite{CP02}.In the spirit of \cite{JK97,N21}, the error detection-correction capability of variable-length codes can be expressed in term of conditions over $τ_{d,k}$. With respect to the prefix metric, the factor one, and every quasi-metric associated to (anti-)automorphisms of the free monoid, we examine whether those conditions are decidable for a given regular code.
Let us Build Bridges: Understanding and Extending Diffusion Generative Models
Venue: arXiv cs.LG 2022
Year: 2022

Diffusion-based generative models have achieved promising results recently, but raise an array of open questions in terms of conceptual understanding, theoretical analysis, algorithm improvement and extensions to discrete, structured, non-Euclidean domains. This work tries to re-exam the overall framework, in order to gain better theoretical understandings and develop algorithmic extensions for data from arbitrary domains. By viewing diffusion models as latent variable models with unobserved diffusion trajectories and applying maximum likelihood estimation (MLE) with latent trajectories imputed from an auxiliary distribution, we show that both the model construction and the imputation of latent trajectories amount to constructing diffusion bridge processes that achieve deterministic values and constraints at end point, for which we provide a systematic study and a suit of tools. Leveraging our framework, we present 1) a first theoretical error analysis for learning diffusion generation models, and 2) a simple and unified approach to learning on data from different discrete and constrained domains. Experiments show that our methods perform superbly on generating images, semantic segments and 3D point clouds.
ELMformer: Efficient Raw Image Restoration with a Locally Multiplicative Transformer
Venue: arXiv cs.CV 2022
Year: 2022

In order to get raw images of high quality for downstream Image Signal Process (ISP), in this paper we present an Efficient Locally Multiplicative Transformer called ELMformer for raw image restoration. ELMformer contains two core designs especially for raw images whose primitive attribute is single-channel. The first design is a Bi-directional Fusion Projection (BFP) module, where we consider both the color characteristics of raw images and spatial structure of single-channel. The second one is that we propose a Locally Multiplicative Self-Attention (L-MSA) scheme to effectively deliver information from the local space to relevant parts. ELMformer can efficiently reduce the computational consumption and perform well on raw image restoration tasks. Enhanced by these two core designs, ELMformer achieves the highest performance and keeps the lowest FLOPs on raw denoising and raw deblurring benchmarks compared with state-of-the-arts. Extensive experiments demonstrate the superiority and generalization ability of ELMformer. On SIDD benchmark, our method has even better denoising performance than ISP-based methods which need huge amount of additional sRGB training images. The codes are release at https://github.com/leonmakise/ELMformer.
P versus B: P Systems as a Formal Framework for Controllability of Boolean Networks
Venue: arXiv cs.FL 2022
Year: 2022

Membrane computing and P systems are a paradigm of massively parallel natural computing introduced by Gheorghe Păun in 1999, inspired by the structure of the living cell and by its biochemical reactions. In spite of this explicit biological motivation, P systems have not been extensively used in modelling real-world systems. To confirm this intuition, we establish a state of the art investigation comparing the use of P systems to that of Boolean networks in this line of research. We then propose to use P systems as a tool for setting up formal frameworks to reason about other formalisms, and we introduce Boolean P systems, specifically tailored for capturing sequential controllability of Boolean networks. We show how to tackle some technical challenges and prove that sequential controllability properly embeds in the framework of Boolean P systems.
On the Generative Capacity of Contextual Grammars with Strictly Locally Testable Selection Languages
Venue: arXiv cs.FL 2022
Year: 2022

We continue the research on the generative capacity of contextual grammars where contexts are adjoined around whole words (externally) or around subwords (internally) which belong to special regular selection languages. All languages generated by contextual grammars where all selection languages are elements of a certain subregular language family form again a language family. We investigate contextual grammars with strictly locally testable selection languages and compare those families to families which are based on finite, monoidal, nilpotent, combinational, definite, suffix-closed, ordered, commutative, circular, non-counting, power-separating, or union-free languages.
3DLG-Detector: 3D Object Detection via Simultaneous Local-Global Feature Learning
Venue: arXiv cs.CV 2022
Year: 2022

Capturing both local and global features of irregular point clouds is essential to 3D object detection (3OD). However, mainstream 3D detectors, e.g., VoteNet and its variants, either abandon considerable local features during pooling operations or ignore many global features in the whole scene context. This paper explores new modules to simultaneously learn local-global features of scene point clouds that serve 3OD positively. To this end, we propose an effective 3OD network via simultaneous local-global feature learning (dubbed 3DLG-Detector). 3DLG-Detector has two key contributions. First, it develops a Dynamic Points Interaction (DPI) module that preserves effective local features during pooling. Besides, DPI is detachable and can be incorporated into existing 3OD networks to boost their performance. Second, it develops a Global Context Aggregation module to aggregate multi-scale features from different layers of the encoder to achieve scene context-awareness. Our method shows improvements over thirteen competitors in terms of detection accuracy and robustness on both the SUN RGB-D and ScanNet datasets. Source code will be available upon publication.
Cadence Detection in Symbolic Classical Music using Graph Neural Networks
Venue: arXiv cs.SD 2022
Year: 2022

Cadences are complex structures that have been driving music from the beginning of contrapuntal polyphony until today. Detecting such structures is vital for numerous MIR tasks such as musicological analysis, key detection, or music segmentation. However, automatic cadence detection remains challenging mainly because it involves a combination of high-level musical elements like harmony, voice leading, and rhythm. In this work, we present a graph representation of symbolic scores as an intermediate means to solve the cadence detection task. We approach cadence detection as an imbalanced node classification problem using a Graph Convolutional Network. We obtain results that are roughly on par with the state of the art, and we present a model capable of making predictions at multiple levels of granularity, from individual notes to beats, thanks to the fine-grained, note-by-note representation. Moreover, our experiments suggest that graph convolution can learn non-local features that assist in cadence detection, freeing us from the need of having to devise specialized features that encode non-local context. We argue that this general approach to modeling musical scores and classification tasks has a number of potential advantages, beyond the specific recognition task presented here.
Binary Representation via Jointly Personalized Sparse Hashing
Venue: arXiv cs.CV 2022
Year: 2022

Unsupervised hashing has attracted much attention for binary representation learning due to the requirement of economical storage and efficiency of binary codes. It aims to encode high-dimensional features in the Hamming space with similarity preservation between instances. However, most existing methods learn hash functions in manifold-based approaches. Those methods capture the local geometric structures (i.e., pairwise relationships) of data, and lack satisfactory performance in dealing with real-world scenarios that produce similar features (e.g. color and shape) with different semantic information. To address this challenge, in this work, we propose an effective unsupervised method, namely Jointly Personalized Sparse Hashing (JPSH), for binary representation learning. To be specific, firstly, we propose a novel personalized hashing module, i.e., Personalized Sparse Hashing (PSH). Different personalized subspaces are constructed to reflect category-specific attributes for different clusters, adaptively mapping instances within the same cluster to the same Hamming space. In addition, we deploy sparse constraints for different personalized subspaces to select important features. We also collect the strengths of the other clusters to build the PSH module with avoiding over-fitting. Then, to simultaneously preserve semantic and pairwise similarities in our JPSH, we incorporate the PSH and manifold-based hash learning into the seamless formulation. As such, JPSH not only distinguishes the instances from different clusters, but also preserves local neighborhood structures within the cluster. Finally, an alternating optimization algorithm is adopted to iteratively capture analytical solutions of the JPSH model. Extensive experiments on four benchmark datasets verify that the JPSH outperforms several hashing algorithms on the similarity search task.
Clairvoyant Regret Minimization: Equivalence with Nemirovski's Conceptual Prox Method and Extension to General Convex Games
Venue: arXiv cs.GT 2022
Year: 2022

A recent paper by Piliouras et al. [2021, 2022] introduces an uncoupled learning algorithm for normal-form games -- called Clairvoyant MWU (CMWU). In this note we show that CMWU is equivalent to the conceptual prox method described by Nemirovski [2004]. This connection immediately shows that it is possible to extend the CMWU algorithm to any convex game, a question left open by Piliouras et al. We call the resulting algorithm -- again equivalent to the conceptual prox method -- Clairvoyant OMD. At the same time, we show that our analysis yields an improved regret bound compared to the original bound by Piliouras et al., in that the regret of CMWU scales only with the square root of the number of players, rather than the number of players themselves.
The Magni Human Motion Dataset: Accurate, Complex, Multi-Modal, Natural, Semantically-Rich and Contextualized
Venue: arXiv cs.RO 2022
Year: 2022

Rapid development of social robots stimulates active research in human motion modeling, interpretation and prediction, proactive collision avoidance, human-robot interaction and co-habitation in shared spaces. Modern approaches to this end require high quality datasets for training and evaluation. However, the majority of available datasets suffers from either inaccurate tracking data or unnatural, scripted behavior of the tracked people. This paper attempts to fill this gap by providing high quality tracking information from motion capture, eye-gaze trackers and on-board robot sensors in a semantically-rich environment. To induce natural behavior of the recorded participants, we utilise loosely scripted task assignment, which induces the participants navigate through the dynamic laboratory environment in a natural and purposeful way. The motion dataset, presented in this paper, sets a high quality standard, as the realistic and accurate data is enhanced with semantic information, enabling development of new algorithms which rely not only on the tracking information but also on contextual cues of the moving agents, static and dynamic environment.
Microwalk-CI: Practical Side-Channel Analysis for JavaScript Applications
Venue: arXiv cs.CR 2022
Year: 2022

Secret-dependent timing behavior in cryptographic implementations has resulted in exploitable vulnerabilities, undermining their security. Over the years, numerous tools to automatically detect timing leakage or even to prove their absence have been proposed. However, a recent study at IEEE S&P 2022 showed that, while many developers are aware of one or more analysis tools, they have major difficulties integrating these into their workflow, as existing tools are tedious to use and mapping discovered leakages to their originating code segments requires expert knowledge. In addition, existing tools focus on compiled languages like C, or analyze binaries, while the industry and open-source community moved to interpreted languages, most notably JavaScript. In this work, we introduce Microwalk-CI, a novel side-channel analysis framework for easy integration into a JavaScript development workflow. First, we extend existing dynamic approaches with a new analysis algorithm, that allows efficient localization and quantification of leakages, making it suitable for use in practical development. We then present a technique for generating execution traces from JavaScript applications, which can be further analyzed with our and other algorithms originally designed for binary analysis. Finally, we discuss how Microwalk-CI can be integrated into a continuous integration (CI) pipeline for efficient and ongoing monitoring. We evaluate our analysis framework by conducting a thorough evaluation of several popular JavaScript cryptographic libraries, and uncover a number of critical leakages.
Segmentation of Weakly Visible Environmental Microorganism Images Using Pair-wise Deep Learning Features
Venue: arXiv cs.CV 2022
Year: 2022

The use of Environmental Microorganisms (EMs) offers a highly efficient, low cost and harmless remedy to environmental pollution, by monitoring and decomposing of pollutants. This relies on how the EMs are correctly segmented and identified. With the aim of enhancing the segmentation of weakly visible EM images which are transparent, noisy and have low contrast, a Pairwise Deep Learning Feature Network (PDLF-Net) is proposed in this study. The use of PDLFs enables the network to focus more on the foreground (EMs) by concatenating the pairwise deep learning features of each image to different blocks of the base model SegNet. Leveraging the Shi and Tomas descriptors, we extract each image's deep features on the patches, which are centered at each descriptor using the VGG-16 model. Then, to learn the intermediate characteristics between the descriptors, pairing of the features is performed based on the Delaunay triangulation theorem to form pairwise deep learning features. In this experiment, the PDLF-Net achieves outstanding segmentation results of 89.24%, 63.20%, 77.27%, 35.15%, 89.72%, 91.44% and 89.30% on the accuracy, IoU, Dice, VOE, sensitivity, precision and specificity, respectively.
Inverse Propensity Score based offline estimator for deterministic ranking lists using position bias
Venue: arXiv cs.IR 2022
Year: 2022

In this work, we present a novel way of computing IPS using a position-bias model for deterministic logging policies. This technique significantly widens the policies on which OPE can be used. We validate this technique using two different experiments on industry-scale data. The OPE results are clearly strongly correlated with the online results, with some constant bias. The estimator requires the examination model to be a reasonably accurate approximation of real user behaviour.
GGArray: A Dynamically Growable GPU Array
Venue: arXiv cs.DC 2022
Year: 2022

We present a dynamically Growable GPU array (GGArray) fully implemented in GPU that does not require synchronization with the host. The idea is to improve the programming of GPU applications that require dynamic memory, by offering a structure that does not require pre-allocating GPU VRAM for the worst case scenario. The GGArray is based on the LFVector, by utilizing an array of them in order to take advantage of the GPU architecture and the synchronization offered by thread blocks. This structure is compared to other state of the art ones such as a pre-allocated static array and a semi-static array that needs to be resized through communication with the host. Experimental evaluation shows that the GGArray has a competitive insertion and resize performance, but it is slower for regular parallel memory accesses. Given the results, the GGArray is a potentially useful structure for applications with high uncertainty on the memory usage as well as applications that have phases, such as an insertion phase followed by a regular GPU phase. In such cases, the GGArray can be used for the first phase and then data can be flattened for the second phase in order to allow the classical GPU memory accesses which are faster. These results constitute a step towards achieving a parallel efficient C++ like vector for modern GPU architectures.
Characterizing Qubit Traffic of a Quantum Intranet aiming at Modular Quantum Computers
Venue: arXiv cs.ET 2022
Year: 2022

Quantum many-core processors are envisioned as the ultimate solution for the scalability of quantum computers. Based upon Noisy Intermediate-Scale Quantum (NISQ) chips interconnected in a sort of quantum intranet, they enable large algorithms to be executed on current and close future technology. In order to optimize such architectures, it is crucial to develop tools that allow specific design space explorations. To this aim, in this paper we present a technique to perform a spatio-temporal characterization of quantum circuits running in multi-chip quantum computers. Specifically, we focus on the analysis of the qubit traffic resulting from operations that involve qubits residing in different cores, and hence quantum communication across chips, while also giving importance to the amount of intra-core operations that occur in between those communications. Using specific multi-core performance metrics and a complete set of benchmarks, our analysis showcases the opportunities that the proposed approach may provide to guide the design of multi-core quantum computers and their interconnects.